scholarly journals Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

2019 ◽  
Vol 9 (24) ◽  
pp. 5523 ◽  
Author(s):  
Luiz Antonio Buschetto Macarini ◽  
Cristian Cechinel ◽  
Matheus Francisco Batista Machado ◽  
Vinicius Faria Culmant Ramos ◽  
Roberto Munoz
Keyword(s):  
At Risk ◽  
Social Presence ◽  
At Risk Students ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Machine Learning Techniques ◽  
Student Interactions ◽  
Learning Techniques ◽  
Significant Difference ◽  
Undergraduate Programs

Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

scholarly journals Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 149464-149478
Author(s):  
Raghad Al-Shabandar ◽  
Abir Jaafar Hussain ◽  
Panos Liatsis ◽  
Robert Keight
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
At Risk Students ◽  
Machine Learning Techniques ◽  
Early Interventions ◽  
Learning Techniques

scholarly journals An Analysis of Discipline and Personality in Blended Environments

2020 ◽  
Vol 46 (1) ◽  
Author(s):  
Chan Chang Tik
Keyword(s):  
Experimental Design ◽  
Personality Traits ◽  
Social Presence ◽  
Personality Trait ◽  
Community Of Inquiry ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Factorial Anova ◽  
Significant Difference ◽  
Main Effects

The purpose of the study is to investigate the interaction between discipline and personality in a blended classroom using the community of inquiry model. To this end, a factorial ANOVA is used to determine the main effects of the high and low of each personality trait as well as the four different clusters of discipline on the presences. The study used a non-experimental design to gather data. A total of 12 lecturers and 408 students from three institutions were involved. The results indicate that there is a significant difference in teaching presence between the hard-applied and hard-pure as well as the hard-applied and soft-pure disciplines only for the conscientiousness personality. Correspondingly, there is a significant difference in social presence between the hard-applied and soft-pure disciplines across all the five personality traits. However, there is no significant difference in cognitive presence for all the discipline clusters across all the personality traits.

scholarly journals COVID-19 Crisis: Exploring Community of Inquiry in Online Learning for Sub-Degree Students

2021 ◽  
Vol 12 ◽  
Author(s):  
Yui-yip Lau ◽  
Yuk Ming Tang ◽  
Ka Yin Chau ◽  
Lina Vyas ◽  
Andres Sandoval-Hernandez ◽  
...  
Keyword(s):  
Online Learning ◽  
Online Education ◽  
Learning Community ◽  
Social Presence ◽  
Community Of Inquiry ◽  
Learning Experience ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Academic Disciplines ◽  
Significant Difference

The COVID-19 pandemic has brought a tremendous impact on the pedagogy and learning experience of students in sub-degree education sector of Hong Kong. Online learning has become the “sole” solution to deal with student learning challenges during this chaotic period. In this study, we explore online learning for sub-degree students by using a community of inquiry (CoI). As such, confirmatory factor analysis (CFA) was conducted on survey data gathered from 287 sub-degree students from the business and engineering disciplines. Results indicated that the network speed for online education determines the perceived cognitive presence, social presence, and teaching presence of students, whereas gender and academic disciplines of students are not moderating factors that create a significant difference in perceived cognitive presence, social presence, and teaching presence of students. Our study findings for creating and sustaining a purposeful online learning community are highlighted.

THE DEVELOPMENT OF A COMMUNITY OF INQUIRY OVER TIME IN AN ONLINE COURSE: UNDERSTANDING THE PROGRESSION AND INTEGRATION OF SOCIAL, COGNITIVE AND TEACHING PRESENCE

2008 ◽  
Vol 12 (3) ◽  
Author(s):  
Zehra Akyol ◽  
D. Randy Garrison
Keyword(s):  
Social Presence ◽  
Community Of Inquiry ◽  
Social Cognitive ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Significant Relationships ◽  
Survey Results ◽  
Community Of Inquiry Framework ◽  
Discussion Postings ◽  
Over Time

The purpose of this study was to explore the dynamics of an online educational experience through the lens of the Community of Inquiry framework. Transcript analysis of online discussion postings and the Community of Inquiry survey were applied in order to understand the progression and integration of each of the Community of Inquiry presences. The results indicated significant change in teaching and social presence categories over time. Moreover, survey results yielded significant relationships among teaching presence, cognitive presence and social presence, and students’ perceived learning and satisfaction in the course.

scholarly journals The teacher as leader: Effect of teaching behaviors on class community and agreement

2013 ◽  
Vol 14 (5) ◽  
Author(s):  
Beth Rubin ◽  
Ronald Fernandes
Keyword(s):  
Social Presence ◽  
Community Of Inquiry ◽  
Online Courses ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Teaching Behaviors ◽  
Group Leaders ◽  
Class Level ◽  
Class Leaders ◽  
Group Agreement

<p>This article examines the effects of teaching behaviors in online university classes, focusing on the agreement among class members. Literature on group leaders’ effects on group agreement about workplace climate is reviewed. Hypotheses are generated about the effects that teachers of online courses, as class leaders, have on both the level and agreement about the community of inquiry. They are tested with a sample of 874 students in 126 online courses. The aggregate class level and strength of agreement about the teaching presence have significant effects on the level and agreement about cognitive presence and social presence. Although the aggregate levels and agreement about community of inquiry are related, different patterns emerge.</p><p>The paper explores the interaction effects of level and agreement, finding that in classes with high levels of teaching presence, the higher the agreement about teaching presence, the higher the agreement about cognitive and social presence especially for classes reporting stronger levels of cognitive and social presence. In classes with lower levels of teaching presence, agreement has a different effect.</p>

scholarly journals Integrating students’ perspectives about online learning: a hierarchy of factors

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Montgomery Van Wart ◽  
Anna Ni ◽  
Pamela Medina ◽  
Jesus Canelon ◽  
Melika Kordrostami ◽  
...  
Keyword(s):  
Online Learning ◽  
Social Presence ◽  
Large Scale ◽  
Success Factors ◽  
Course Design ◽  
Instructional Support ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Future Research ◽  
Social Comfort

AbstractThis article reports on a large-scale (n = 987), exploratory factor analysis study incorporating various concepts identified in the literature as critical success factors for online learning from the students’ perspective, and then determines their hierarchical significance. Seven factors--Basic Online Modality, Instructional Support, Teaching Presence, Cognitive Presence, Online Social Comfort, Online Interactive Modality, and Social Presence--were identified as significant and reliable. Regression analysis indicates the minimal factors for enrollment in future classes—when students consider convenience and scheduling—were Basic Online Modality, Cognitive Presence, and Online Social Comfort. Students who accepted or embraced online courses on their own merits wanted a minimum of Basic Online Modality, Teaching Presence, Cognitive Presence, Online Social Comfort, and Social Presence. Students, who preferred face-to-face classes and demanded a comparable experience, valued Online Interactive Modality and Instructional Support more highly. Recommendations for online course design, policy, and future research are provided.

Projecting conflict risk following the Shared Socioeconomic pathways: what role for water stress and climate?

2021 ◽  
Author(s):  
Sophie de Bruin ◽  
Jannis Hoch ◽  
Nina von Uexkull ◽  
Halvard Buhaug ◽  
Nico Wanders
Keyword(s):  
Climate Change ◽  
At Risk ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Related Factors ◽  
Socioeconomic Impacts ◽  
Learning Techniques ◽  
The Future ◽  
Conflict Risk

&lt;p&gt;The socioeconomic impacts of&amp;#160;changes in climate-related&amp;#160;and&amp;#160;hydrology-related factors&amp;#160;are increasingly&amp;#160;acknowledged&amp;#160;to affect&amp;#160;the&amp;#160;on-set&amp;#160;of&amp;#160;violent&amp;#160;conflict.&amp;#160;Full consensus&amp;#160;upon&amp;#160;the general&amp;#160;mechanisms&amp;#160;linking&amp;#160;these&amp;#160;factors&amp;#160;with conflict&amp;#160;is,&amp;#160;however,&amp;#160;still limited.&amp;#160;The absence of full&amp;#160;understanding&amp;#160;of&amp;#160;the non-linearities&amp;#160;between all components and the lack of sufficient data make it&amp;#160;therefore&amp;#160;hard to address violent conflict risk on the long-term.&amp;#160;&lt;/p&gt;&lt;p&gt;Although it is&amp;#160;neither&amp;#160;desirable nor feasible&amp;#160;to make exact predictions,&amp;#160;projections are a viable means&amp;#160;to provide&amp;#160;insights into potential&amp;#160;future&amp;#160;conflict risks&amp;#160;and uncertainties thereof.&amp;#160;Hence, making&amp;#160;different&amp;#160;projections is a&amp;#8239;legitimate&amp;#8239;way to deal with and understand these uncertainties, since the construction of diverse scenarios delivers insights into&amp;#160;possible realizations of the future.&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;Through&amp;#160;machine learning techniques, we&amp;#8239;(re)assess the major drivers of conflict&amp;#160;for the current situation&amp;#160;in Africa, which are&amp;#160;then&amp;#160;applied to project the regions-at-risk following&amp;#160;different&amp;#160;scenarios.&amp;#160;The model shows to accurately reproduce observed historic patterns leading to a&amp;#160;high ROC score of&amp;#160;0.91.&amp;#160;We show that&amp;#160;socio-economic factors&amp;#160;are&amp;#160;most dominant&amp;#160;when&amp;#160;projecting&amp;#160;conflicts&amp;#160;over&amp;#160;the African continent.&amp;#160;The projections show that there is an&amp;#160;overall&amp;#160;reduction in conflict risk&amp;#160;as a result of&amp;#160;increased&amp;#160;economic welfare that&amp;#160;offsets&amp;#160;the&amp;#160;adverse&amp;#160;impacts&amp;#160;of&amp;#160;climate change and&amp;#160;hydrologic variables.&amp;#160;It must be noted, however, that these projections are based on current relations.&amp;#160;In case the relations of drivers and conflict change in the future, the resulting&amp;#160;regions-at-risk may change too.&amp;#160;&amp;#160; By identifying the most prominent drivers,&amp;#160;conflict risk&amp;#160;mitigation measures can be tuned more accurately to reduce the direct and indirect consequences of climate change&amp;#160;on&amp;#160;the population in Africa.&amp;#160;As new and improved&amp;#160;data becomes available, the model can be updated for more robust projections of conflict risk in Africa under climate change.&lt;/p&gt;

Fostering Social Presence in a Blended Learning Faculty Development Institute

2013 ◽  
pp. 374-388
Author(s):  
David S. Goldstein ◽  
Carol Leppa ◽  
Andreas Brockhaus ◽  
Rebecca Bliquez ◽  
Ian Porter
Keyword(s):  
Blended Learning ◽  
Social Presence ◽  
Community Of Inquiry ◽  
Faculty Members ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Course Development ◽  
Hybrid Course ◽  
Blended Courses ◽  
Development Institute

To help faculty develop well-designed blended courses, the Community of Inquiry (CoI) framework (Garrison, Anderson & Archer, 2000) was used to design and deliver a ten-week Hybrid Course Development Institute (HCDI) for faculty members from a variety of disciplines. The faculty experienced a blended format and developed courses based on the three components of the CoI framework: cognitive presence, teaching presence, and social presence, the last of which is particularly challenging to achieve. This chapter provides an overview of the HCDI structure, content, and assessment, and suggests ways to foster social presence in and beyond a blended learning institute for faculty members.

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